Split (2)

train · 433k rows

id stringlengths 9 16	title stringlengths 4 278	abstract stringlengths 3 4.08k	cs.HC bool 2 classes	cs.CE bool 2 classes	cs.SD bool 2 classes	cs.SI bool 2 classes	cs.AI bool 2 classes	cs.IR bool 2 classes	cs.LG bool 2 classes	cs.RO bool 2 classes	cs.CL bool 2 classes	cs.IT bool 2 classes	cs.SY bool 2 classes	cs.CV bool 2 classes	cs.CR bool 2 classes	cs.CY bool 2 classes	cs.MA bool 2 classes	cs.NE bool 2 classes	cs.DB bool 2 classes	Other bool 2 classes	__index_level_0__ int64 0 541k
2407.01996	ViG-Bias: Visually Grounded Bias Discovery and Mitigation	The proliferation of machine learning models in critical decision making processes has underscored the need for bias discovery and mitigation strategies. Identifying the reasons behind a biased system is not straightforward, since in many occasions they are associated with hidden spurious correlations which are not eas...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	469,543
2205.10798	PAC-Wrap: Semi-Supervised PAC Anomaly Detection	Anomaly detection is essential for preventing hazardous outcomes for safety-critical applications like autonomous driving. Given their safety-criticality, these applications benefit from provable bounds on various errors in anomaly detection. To achieve this goal in the semi-supervised setting, we propose to provide Pr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	297,878
1711.00532	SCDA: School Compatibility Decomposition Algorithm for Solving the Multi-School Bus Routing and Scheduling Problem	Safely serving the school transportation demand with the minimum number of buses is one of the highest financial goals of school transportation directors. To achieve that objective, a good and efficient way to solve the routing and scheduling problem is required. Due to the growth of the computing power, the spotlight ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	83,734
2412.02689	Preliminary Investigation into Data Scaling Laws for Imitation Learning-Based End-to-End Autonomous Driving	The end-to-end autonomous driving paradigm has recently attracted lots of attention due to its scalability. However, existing methods are constrained by the limited scale of real-world data, which hinders a comprehensive exploration of the scaling laws associated with end-to-end autonomous driving. To address this issu...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	513,639
2010.15025	Non-Autoregressive Transformer ASR with CTC-Enhanced Decoder Input	Non-autoregressive (NAR) transformer models have achieved significantly inference speedup but at the cost of inferior accuracy compared to autoregressive (AR) models in automatic speech recognition (ASR). Most of the NAR transformers take a fixed-length sequence filled with MASK tokens or a redundant sequence copied fr...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	203,648
2402.18302	EchoTrack: Auditory Referring Multi-Object Tracking for Autonomous Driving	This paper introduces the task of Auditory Referring Multi-Object Tracking (AR-MOT), which dynamically tracks specific objects in a video sequence based on audio expressions and appears as a challenging problem in autonomous driving. Due to the lack of semantic modeling capacity in audio and video, existing works have ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	433,377
2110.14842	Towards the ultimate limits of quantum channel discrimination	This note studies the difficulty of discriminating quantum channels under operational regimes. First, we make a conjecture on the exponentially strong converse of quantum channel hypothesis testing under coherent strategies, meaning that any strategy to make the Type II error decays with an exponent larger than the reg...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	263,660
2501.04517	Histogram-Equalized Quantization for logic-gated Residual Neural Networks	Adjusting the quantization according to the data or to the model loss seems mandatory to enable a high accuracy in the context of quantized neural networks. This work presents Histogram-Equalized Quantization (HEQ), an adaptive framework for linear symmetric quantization. HEQ automatically adapts the quantization thres...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	523,251
2209.12244	Multimodal Channel-Mixing: Channel and Spatial Masked AutoEncoder on Facial Action Unit Detection	Recent studies have focused on utilizing multi-modal data to develop robust models for facial Action Unit (AU) detection. However, the heterogeneity of multi-modal data poses challenges in learning effective representations. One such challenge is extracting relevant features from multiple modalities using a single feat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	319,465
2409.18257	Developing a Dual-Stage Vision Transformer Model for Lung Disease Classification	Lung diseases have become a prevalent problem throughout the United States, affecting over 34 million people. Accurate and timely diagnosis of the different types of lung diseases is critical, and Artificial Intelligence (AI) methods could speed up these processes. A dual-stage vision transformer is built throughout th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	492,161
2107.02070	Antithetic Riemannian Manifold And Quantum-Inspired Hamiltonian Monte Carlo	Markov Chain Monte Carlo inference of target posterior distributions in machine learning is predominately conducted via Hamiltonian Monte Carlo and its variants. This is due to Hamiltonian Monte Carlo based samplers ability to suppress random-walk behaviour. As with other Markov Chain Monte Carlo methods, Hamiltonian M...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	244,691
0903.1139	The Complexity of Reasoning with Global Constraints	Constraint propagation is one of the techniques central to the success of constraint programming. To reduce search, fast algorithms associated with each constraint prune the domains of variables. With global (or non-binary) constraints, the cost of such propagation may be much greater than the quadratic cost for binary...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	3,297
2104.09556	Removing Diffraction Image Artifacts in Under-Display Camera via Dynamic Skip Connection Network	Recent development of Under-Display Camera (UDC) systems provides a true bezel-less and notch-free viewing experience on smartphones (and TV, laptops, tablets), while allowing images to be captured from the selfie camera embedded underneath. In a typical UDC system, the microstructure of the semi-transparent organic li...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	231,270
2303.14628	Multi-Frame Self-Supervised Depth Estimation with Multi-Scale Feature Fusion in Dynamic Scenes	Multi-frame methods improve monocular depth estimation over single-frame approaches by aggregating spatial-temporal information via feature matching. However, the spatial-temporal feature leads to accuracy degradation in dynamic scenes. To enhance the performance, recent methods tend to propose complex architectures fo...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	354,178
2103.05150	Orientation to Pose: Continuum Robots Shape Sensing Based on Piecewise Polynomial Curvature Model	Continuum robots are typically slender and flexible with infinite freedoms in theory, which poses a challenge for their control and application. The shape sensing of continuum robots is vital to realise accuracy control. This letter proposed a novel general real-time shape sensing framework of continuum robots based on...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	223,882
1003.0445	On The Design of Signature Codes in Decentralized Wireless Networks	This paper addresses a unified approach towards communication in decentralized wireless networks of separate transmitter-receiver pairs. In general, users are unaware of each other's codebooks and there is no central controller to assign the resources in the network to the users. A randomized signaling scheme is introd...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	5,819
2110.11891	On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning	Machine unlearning, i.e. having a model forget about some of its training data, has become increasingly more important as privacy legislation promotes variants of the right-to-be-forgotten. In the context of deep learning, approaches for machine unlearning are broadly categorized into two classes: exact unlearning meth...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	262,645
2402.03774	Learning a Decision Tree Algorithm with Transformers	Decision trees are renowned for their ability to achieve high predictive performance while remaining interpretable, especially on tabular data. Traditionally, they are constructed through recursive algorithms, where they partition the data at every node in a tree. However, identifying a good partition is challenging, a...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	427,186
0708.3920	Kinematic analysis of the 3-RPR parallel manipulator	The aim of this paper is the kinematic study of a 3-RPR planar parallel manipulator where the three fixed revolute joints are actuated. The direct and inverse kinematic problem as well as the singular configuration is characterized. On parallel singular configurations, the motion produce by the mobile platform can be c...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	609
1205.4875	A New Approach Towards the Golomb-Welch Conjecture	The Golomb-Welch conjecture deals with the existence of perfect $e$% -error correcting Lee codes of word length $n,$ $PL(n,e)$ codes. Although there are many papers on the topic, the conjecture is still far from being solved. In this paper we initiate the study of an invariant connected to abelian groups that enables u...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	16,125
2308.03908	ViLP: Knowledge Exploration using Vision, Language, and Pose Embeddings for Video Action Recognition	Video Action Recognition (VAR) is a challenging task due to its inherent complexities. Though different approaches have been explored in the literature, designing a unified framework to recognize a large number of human actions is still a challenging problem. Recently, Multi-Modal Learning (MML) has demonstrated promis...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	384,211
2402.03220	The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents	We investigate the training dynamics of two-layer neural networks when learning multi-index target functions. We focus on multi-pass gradient descent (GD) that reuses the batches multiple times and show that it significantly changes the conclusion about which functions are learnable compared to single-pass gradient des...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	426,908
2001.10516	Tri-graph Information Propagation for Polypharmacy Side Effect Prediction	The use of drug combinations often leads to polypharmacy side effects (POSE). A recent method formulates POSE prediction as a link prediction problem on a graph of drugs and proteins, and solves it with Graph Convolutional Networks (GCNs). However, due to the complex relationships in POSE, this method has high computat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	161,845
2411.15100	XGrammar: Flexible and Efficient Structured Generation Engine for Large Language Models	The applications of LLM Agents are becoming increasingly complex and diverse, leading to a high demand for structured outputs that can be parsed into code, structured function calls, and embodied agent commands. These developments bring significant demands for structured generation in LLM inference. Context-free gramma...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	510,437
2104.13293	Evidential segmentation of 3D PET/CT images	PET and CT are two modalities widely used in medical image analysis. Accurately detecting and segmenting lymphomas from these two imaging modalities are critical tasks for cancer staging and radiotherapy planning. However, this task is still challenging due to the complexity of PET/CT images, and the computation cost t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	232,461
1312.6130	A Functional View of Strong Negation in Answer Set Programming	The distinction between strong negation and default negation has been useful in answer set programming. We present an alternative account of strong negation, which lets us view strong negation in terms of the functional stable model semantics by Bartholomew and Lee. More specifically, we show that, under complete inter...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	29,320
1810.06765	A survey of automatic de-identification of longitudinal clinical narratives	Use of medical data, also known as electronic health records, in research helps develop and advance medical science. However, protecting patient confidentiality and identity while using medical data for analysis is crucial. Medical data can be in the form of tabular structures (i.e. tables), free-form narratives, and i...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	110,499
2110.12798	Variational Gaussian Processes: A Functional Analysis View	Variational Gaussian process (GP) approximations have become a standard tool in fast GP inference. This technique requires a user to select variational features to increase efficiency. So far the common choices in the literature are disparate and lacking generality. We propose to view the GP as lying in a Banach space ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	262,972
2205.11672	Why does Throwing Away Data Improve Worst-Group Error?	When facing data with imbalanced classes or groups, practitioners follow an intriguing strategy to achieve best results. They throw away examples until the classes or groups are balanced in size, and then perform empirical risk minimization on the reduced training set. This opposes common wisdom in learning theory, whe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	298,227
2407.09777	Graph Transformers: A Survey	Graph transformers are a recent advancement in machine learning, offering a new class of neural network models for graph-structured data. The synergy between transformers and graph learning demonstrates strong performance and versatility across various graph-related tasks. This survey provides an in-depth review of rec...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	472,711
1407.2812	Rate-Optimal Detection of Very Short Signal Segments	Motivated by a range of applications in engineering and genomics, we consider in this paper detection of very short signal segments in three settings: signals with known shape, arbitrary signals, and smooth signals. Optimal rates of detection are established for the three cases and rate-optimal detectors are constructe...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	34,566
2109.02517	Error Controlled Actor-Critic	On error of value function inevitably causes an overestimation phenomenon and has a negative impact on the convergence of the algorithms. To mitigate the negative effects of the approximation error, we propose Error Controlled Actor-critic which ensures confining the approximation error in value function. We present an...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	253,776
2206.08724	Crowdsourcing Relative Rankings of Multi-Word Expressions: Experts versus Non-Experts	In this study we investigate to which degree experts and non-experts agree on questions of difficulty in a crowdsourcing experiment. We ask non-experts (second language learners of Swedish) and two groups of experts (teachers of Swedish as a second/foreign language and CEFR experts) to rank multi-word expressions in a ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	303,266
2407.15472	Learning deep illumination-robust features from multispectral filter array images	Multispectral (MS) snapshot cameras equipped with a MS filter array (MSFA), capture multiple spectral bands in a single shot, resulting in a raw mosaic image where each pixel holds only one channel value. The fully-defined MS image is estimated from the raw one through \textit{demosaicing}, which inevitably introduces ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	475,199
2103.04469	Local word statistics affect reading times independently of surprisal	Surprisal theory has provided a unifying framework for understanding many phenomena in sentence processing (Hale, 2001; Levy, 2008a), positing that a word's conditional probability given all prior context fully determines processing difficulty. Problematically for this claim, one local statistic, word frequency, has al...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	223,644
2309.06188	Computer Vision Pipeline for Automated Antarctic Krill Analysis	British Antarctic Survey (BAS) researchers launch annual expeditions to the Antarctic in order to estimate Antarctic Krill biomass and assess the change from previous years. These comparisons provide insight into the effects of the current environment on this key component of the marine food chain. In this work we have...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	391,341
2409.08202	What Makes a Maze Look Like a Maze?	A unique aspect of human visual understanding is the ability to flexibly interpret abstract concepts: acquiring lifted rules explaining what they symbolize, grounding them across familiar and unfamiliar contexts, and making predictions or reasoning about them. While off-the-shelf vision-language models excel at making ...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	487,807
2112.08789	Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages	Cognates are variants of the same lexical form across different languages; for example 'fonema' in Spanish and 'phoneme' in English are cognates, both of which mean 'a unit of sound'. The task of automatic detection of cognates among any two languages can help downstream NLP tasks such as Cross-lingual Information Retr...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	271,938
2207.05321	Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures	Deep neural networks have been found vulnerable to adversarial attacks, thus raising potentially concerns in security-sensitive contexts. To address this problem, recent research has investigated the adversarial robustness of deep neural networks from the architectural point of view. However, searching for architecture...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	307,499
2201.10095	RecShard: Statistical Feature-Based Memory Optimization for Industry-Scale Neural Recommendation	We propose RecShard, a fine-grained embedding table (EMB) partitioning and placement technique for deep learning recommendation models (DLRMs). RecShard is designed based on two key observations. First, not all EMBs are equal, nor all rows within an EMB are equal in terms of access patterns. EMBs exhibit distinct memor...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	276,876
2107.11020	Emotion analysis and detection during COVID-19	Crises such as natural disasters, global pandemics, and social unrest continuously threaten our world and emotionally affect millions of people worldwide in distinct ways. Understanding emotions that people express during large-scale crises helps inform policy makers and first responders about the emotional states of t...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	247,474
2405.17955	Efficient Prior Calibration From Indirect Data	Bayesian inversion is central to the quantification of uncertainty within problems arising from numerous applications in science and engineering. To formulate the approach, four ingredients are required: a forward model mapping the unknown parameter to an element of a solution space, often the solution space for a diff...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	458,206
2012.13504	LMMSE Processing for Cell-free Massive MIMO with Radio Stripes and MRC Fronthaul	Cell-free massive MIMO provides ubiquitous connectivity for multiple users, and implementation using radio stripes is very efficient. Compared with collocated massive MIMO, the major cost includes fronthaul overheads and AP hardware. Maximum ratio combination (MRC) achieves a low fronthaul loading and low-cost AP, but ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	213,236
1912.02914	RED-NET: A Recursive Encoder-Decoder Network for Edge Detection	In this paper, we introduce RED-NET: A Recursive Encoder-Decoder Network with Skip-Connections for edge detection in natural images. The proposed network is a novel integration of a Recursive Neural Network with an Encoder-Decoder architecture. The recursive network enables us to increase the network depth without incr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	156,464
2401.03085	Consensus-Threshold Criterion for Offline Signature Verification using Convolutional Neural Network Learned Representations	A genuine signer's signature is naturally unstable even at short time-intervals whereas, expert forgers always try to perfectly mimic a genuine signer's signature. This presents a challenge which puts a genuine signer at risk of being denied access, while a forge signer is granted access. The implication is a high fals...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	419,959
2411.14639	Differentially Private Adaptation of Diffusion Models via Noisy Aggregated Embeddings	We introduce novel methods for adapting diffusion models under differential privacy (DP) constraints, enabling privacy-preserving style and content transfer without fine-tuning. Traditional approaches to private adaptation, such as DP-SGD, incur significant computational overhead and degrade model performance when appl...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	510,257
2203.04111	Plumeria at SemEval-2022 Task 6: Robust Approaches for Sarcasm Detection for English and Arabic Using Transformers and Data Augmentation	This paper describes our submission to SemEval-2022 Task 6 on sarcasm detection and its five subtasks for English and Arabic. Sarcasm conveys a meaning which contradicts the literal meaning, and it is mainly found on social networks. It has a significant role in understanding the intention of the user. For detecting sa...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	284,352
1811.04129	STA: Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification	In this work, we propose a novel Spatial-Temporal Attention (STA) approach to tackle the large-scale person re-identification task in videos. Different from the most existing methods, which simply compute representations of video clips using frame-level aggregation (e.g. average pooling), the proposed STA adopts a more...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	112,998
2404.12810	Enhancing Counterfactual Explanation Search with Diffusion Distance and Directional Coherence	A pressing issue in the adoption of AI models is the increasing demand for more human-centric explanations of their predictions. To advance towards more human-centric explanations, understanding how humans produce and select explanations has been beneficial. In this work, inspired by insights of human cognition we prop...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	448,044
2410.08218	A Visual-Analytical Approach for Automatic Detection of Cyclonic Events in Satellite Observations	Estimating the location and intensity of tropical cyclones holds crucial significance for predicting catastrophic weather events. In this study, we approach this task as a detection and regression challenge, specifically over the North Indian Ocean (NIO) region where best tracks location and wind speed information serv...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	497,012
2012.14736	Present-Biased Optimization	This paper explores the behavior of present-biased agents, that is, agents who erroneously anticipate the costs of future actions compared to their real costs. Specifically, the paper extends the original framework proposed by Akerlof (1991) for studying various aspects of human behavior related to time-inconsistent pl...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	213,592
2005.13787	Assessing Centrality Without Knowing Connections	We consider the privacy-preserving computation of node influence in distributed social networks, as measured by egocentric betweenness centrality (EBC). Motivated by modern communication networks spanning multiple providers, we show for the first time how multiple mutually-distrusting parties can successfully compute n...	false	false	false	true	false	false	true	false	false	false	false	false	true	false	false	false	false	false	179,100
2105.11601	Personalized Transformer for Explainable Recommendation	Personalization of natural language generation plays a vital role in a large spectrum of tasks, such as explainable recommendation, review summarization and dialog systems. In these tasks, user and item IDs are important identifiers for personalization. Transformer, which is demonstrated with strong language modeling c...	false	false	false	false	true	true	true	false	true	false	false	false	false	false	false	false	false	false	236,753
2106.03722	Error Loss Networks	A novel model called error loss network (ELN) is proposed to build an error loss function for supervised learning. The ELN is in structure similar to a radial basis function (RBF) neural network, but its input is an error sample and output is a loss corresponding to that error sample. That means the nonlinear input-out...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	239,422
1611.06009	Fuzzy Statistical Matrices for Cell Classification	In this paper, we generalize image (texture) statistical descriptors and propose algorithms that improve their efficacy. Recently, a new method showed how the popular Co-Occurrence Matrix (COM) can be modified into a fuzzy version (FCOM) which is more effective and robust to noise. Here, we introduce new fuzzy versions...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	64,111
1712.00900	On the Effect of Shadowing Correlation on Wireless Network Performance	We propose and analyze a new shadowing field model meant to capture spatial correlations. The interference field associated with this new model is compared to that of the widely used independent shadowing model. Independent shadowing over links is adopted because of the resulting closed forms for performance metrics, a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	86,003
2003.05837	Top-1 Solution of Multi-Moments in Time Challenge 2019	In this technical report, we briefly introduce the solutions of our team 'Efficient' for the Multi-Moments in Time challenge in ICCV 2019. We first conduct several experiments with popular Image-Based action recognition methods TRN, TSN, and TSM. Then a novel temporal interlacing network is proposed towards fast and ac...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	167,962
2009.10484	Asymptotically Optimal Sampling-Based Motion Planning Methods	Motion planning is a fundamental problem in autonomous robotics that requires finding a path to a specified goal that avoids obstacles and takes into account a robot's limitations and constraints. It is often desirable for this path to also optimize a cost function, such as path length. Formal path-quality guarantees...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	196,905
2412.15826	Using matrix-product states for time-series machine learning	Matrix-product states (MPS) have proven to be a versatile ansatz for modeling quantum many-body physics. For many applications, and particularly in one-dimension, they capture relevant quantum correlations in many-body wavefunctions while remaining tractable to store and manipulate on a classical computer. This has mot...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	519,279
1107.5951	Optimal, scalable forward models for computing gravity anomalies	We describe three approaches for computing a gravity signal from a density anomaly. The first approach consists of the classical "summation" technique, whilst the remaining two methods solve the Poisson problem for the gravitational potential using either a Finite Element (FE) discretization employing a multilevel prec...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	11,508
2406.02950	Joint Beam Search Integrating CTC, Attention, and Transducer Decoders	End-to-end automatic speech recognition (E2E-ASR) can be classified by its decoder architectures, such as connectionist temporal classification (CTC), recurrent neural network transducer (RNN-T), attention-based encoder-decoder, and Mask-CTC models. Each decoder architecture has advantages and disadvantages, leading pr...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	461,017
2403.00748	Primal-Dual iLQR	We introduce a new algorithm for solving unconstrained discrete-time optimal control problems. Our method follows a direct multiple shooting approach, and consists of applying the SQP method together with an $\ell_2$ augmented Lagrangian primal-dual merit function. We use the LQR algorithm to efficiently solve the prim...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	434,082
2406.17697	HGTDP-DTA: Hybrid Graph-Transformer with Dynamic Prompt for Drug-Target Binding Affinity Prediction	Drug target binding affinity (DTA) is a key criterion for drug screening. Existing experimental methods are time-consuming and rely on limited structural and domain information. While learning-based methods can model sequence and structural information, they struggle to integrate contextual data and often lack comprehe...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	467,682
1806.07057	Effect of Hyper-Parameter Optimization on the Deep Learning Model Proposed for Distributed Attack Detection in Internet of Things Environment	This paper studies the effect of various hyper-parameters and their selection for the best performance of the deep learning model proposed in [1] for distributed attack detection in the Internet of Things (IoT). The findings show that there are three hyper-parameters that have more influence on the best performance ach...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	100,825
1502.06075	A new network-based algorithm for human activity recognition in video	In this paper, a new network-transmission-based (NTB) algorithm is proposed for human activity recognition in videos. The proposed NTB algorithm models the entire scene as an error-free network. In this network, each node corresponds to a patch of the scene and each edge represents the activity correlation between the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	40,445
2005.13189	Decentralized Optimization On Time-Varying Directed Graphs Under Communication Constraints	We consider the problem of decentralized optimization where a collection of agents, each having access to a local cost function, communicate over a time-varying directed network and aim to minimize the sum of those functions. In practice, the amount of information that can be exchanged between the agents is limited due...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	178,941
1702.03222	Mining Electronic Health Records: A Survey	The continuously increasing cost of the US healthcare system has received significant attention. Central to the ideas aimed at curbing this trend is the use of technology, in the form of the mandate to implement electronic health records (EHRs). EHRs consist of patient information such as demographics, medications, lab...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	68,092
1807.07282	Anomaly Detection for Water Treatment System based on Neural Network with Automatic Architecture Optimization	We continue to develop our neural network (NN) based forecasting approach to anomaly detection (AD) using the Secure Water Treatment (SWaT) industrial control system (ICS) testbed dataset. We propose genetic algorithms (GA) to find the best NN architecture for a given dataset, using the NAB metric to assess the quality...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	103,288
cs/0703035	On the Distortion SNR Exponent of Some Layered Transmission Schemes	We consider the problem of joint source-channel coding for transmitting K samples of a complex Gaussian source over T = bK uses of a block-fading multiple input multiple output (MIMO) channel with M transmit and N receive antennas. We consider the case when we are allowed to code over L blocks. The channel gain is assu...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	540,218
2211.08717	SWIN-SFTNet : Spatial Feature Expansion and Aggregation using Swin Transformer For Whole Breast micro-mass segmentation	Incorporating various mass shapes and sizes in training deep learning architectures has made breast mass segmentation challenging. Moreover, manual segmentation of masses of irregular shapes is time-consuming and error-prone. Though Deep Neural Network has shown outstanding performance in breast mass segmentation, it f...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	330,744
2310.02065	VENOM: A Vectorized N:M Format for Unleashing the Power of Sparse Tensor Cores	The increasing success and scaling of Deep Learning models demands higher computational efficiency and power. Sparsification can lead to both smaller models as well as higher compute efficiency, and accelerated hardware is becoming available. However, exploiting it efficiently requires kernel implementations, pruning a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	396,696
2111.13850	Temporal Context Mining for Learned Video Compression	We address end-to-end learned video compression with a special focus on better learning and utilizing temporal contexts. For temporal context mining, we propose to store not only the previously reconstructed frames, but also the propagated features into the generalized decoded picture buffer. From the stored propagated...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	268,409
2110.09936	NeuralDiff: Segmenting 3D objects that move in egocentric videos	Given a raw video sequence taken from a freely-moving camera, we study the problem of decomposing the observed 3D scene into a static background and a dynamic foreground containing the objects that move in the video sequence. This task is reminiscent of the classic background subtraction problem, but is significantly h...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	261,981
2303.12343	LD-ZNet: A Latent Diffusion Approach for Text-Based Image Segmentation	Large-scale pre-training tasks like image classification, captioning, or self-supervised techniques do not incentivize learning the semantic boundaries of objects. However, recent generative foundation models built using text-based latent diffusion techniques may learn semantic boundaries. This is because they have to ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	353,231
2410.04721	ACDC: Autoregressive Coherent Multimodal Generation using Diffusion Correction	Autoregressive models (ARMs) and diffusion models (DMs) represent two leading paradigms in generative modeling, each excelling in distinct areas: ARMs in global context modeling and long-sequence generation, and DMs in generating high-quality local contexts, especially for continuous data such as images and short video...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	495,412
2410.12843	Exploring Prompt Engineering: A Systematic Review with SWOT Analysis	In this paper, we conduct a comprehensive SWOT analysis of prompt engineering techniques within the realm of Large Language Models (LLMs). Emphasizing linguistic principles, we examine various techniques to identify their strengths, weaknesses, opportunities, and threats. Our findings provide insights into enhancing AI...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	499,231
1906.06047	Dynamic Term-Modal Logics for First-Order Epistemic Planning	Many classical planning frameworks are built on first-order languages. The first-order expressive power is desirable for compactly representing actions via schemas, and for specifying quantified conditions such as $\neg\exists x\mathsf{blocks\_door}(x)$. In contrast, several recent epistemic planning frameworks are bui...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	true	135,198
2209.13012	Survey on Fairness Notions and Related Tensions	Automated decision systems are increasingly used to take consequential decisions in problems such as job hiring and loan granting with the hope of replacing subjective human decisions with objective machine learning (ML) algorithms. However, ML-based decision systems are prone to bias, which results in yet unfair decis...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	319,735
2407.20274	Exploring the Plausibility of Hate and Counter Speech Detectors with Explainable AI	In this paper we investigate the explainability of transformer models and their plausibility for hate speech and counter speech detection. We compare representatives of four different explainability approaches, i.e., gradient-based, perturbation-based, attention-based, and prototype-based approaches, and analyze them q...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	477,122
2303.12992	A Survey of Historical Learning: Learning Models with Learning History	New knowledge originates from the old. The various types of elements, deposited in the training history, are a large amount of wealth for improving learning deep models. In this survey, we comprehensively review and summarize the topic--``Historical Learning: Learning Models with Learning History'', which learns better...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	353,489
2306.08766	A Copernican Revolution in Data	Half a century ago, Charles Bachman foresaw the significance and centrality of data in the digital world. In this short paper, we delve into the evolution of these ideas within the database community over the past decades. We believe that this historical analysis helps deepen our comprehension of the fundamental change...	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	false	373,534
1507.04811	Lift-Based Bidding in Ad Selection	Real-time bidding (RTB) has become one of the largest online advertising markets in the world. Today the bid price per ad impression is typically decided by the expected value of how it can lead to a desired action event (e.g., registering an account or placing a purchase order) to the advertiser. However, this industr...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	45,217
2006.07995	BatVision with GCC-PHAT Features for Better Sound to Vision Predictions	Inspired by sophisticated echolocation abilities found in nature, we train a generative adversarial network to predict plausible depth maps and grayscale layouts from sound. To achieve this, our sound-to-vision model processes binaural echo-returns from chirping sounds. We build upon previous work with BatVision that c...	false	false	true	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	182,033
2403.02253	KnowPhish: Large Language Models Meet Multimodal Knowledge Graphs for Enhancing Reference-Based Phishing Detection	Phishing attacks have inflicted substantial losses on individuals and businesses alike, necessitating the development of robust and efficient automated phishing detection approaches. Reference-based phishing detectors (RBPDs), which compare the logos on a target webpage to a known set of logos, have emerged as the stat...	false	false	false	false	true	false	true	false	true	false	false	false	true	false	false	false	false	false	434,745
2203.07856	Improved Multi-label Classification under Temporal Concept Drift: Rethinking Group-Robust Algorithms in a Label-Wise Setting	In document classification for, e.g., legal and biomedical text, we often deal with hundreds of classes, including very infrequent ones, as well as temporal concept drift caused by the influence of real world events, e.g., policy changes, conflicts, or pandemics. Class imbalance and drift can sometimes be mitigated by ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	285,587
2204.13176	Divisible Codes for Quantum Computation	Divisible codes are defined by the property that codeword weights share a common divisor greater than one. They are used to design signals for communications and sensing, and this paper explores how they can be used to protect quantum information as it is transformed by logical gates. Given a CSS code $\mathcal{C}$, we...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	293,724
1703.01310	Count-Based Exploration with Neural Density Models	Bellemare et al. (2016) introduced the notion of a pseudo-count, derived from a density model, to generalize count-based exploration to non-tabular reinforcement learning. This pseudo-count was used to generate an exploration bonus for a DQN agent and combined with a mixed Monte Carlo update was sufficient to achieve s...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	69,338
2109.10279	Ranking Feature-Block Importance in Artificial Multiblock Neural Networks	In artificial neural networks, understanding the contributions of input features on the prediction fosters model explainability and delivers relevant information about the dataset. While typical setups for feature importance ranking assess input features individually, in this study, we go one step further and rank the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	256,568
2304.11834	Robust Tickets Can Transfer Better: Drawing More Transferable Subnetworks in Transfer Learning	Transfer learning leverages feature representations of deep neural networks (DNNs) pretrained on source tasks with rich data to empower effective finetuning on downstream tasks. However, the pretrained models are often prohibitively large for delivering generalizable representations, which limits their deployment on ed...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	359,990
1911.09548	A parallel space-time multigrid method for the eddy-current equation	We expand the applicabilities and capabilities of an already existing space-time parallel method based on a block Jacobi smoother. First we formulate a more detailed criterion for spatial coarsening, which enables the method to deal with unstructured meshes and varying material parameters. Further we investigate the ap...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	154,553
1710.06512	Pose-based Deep Gait Recognition	Human gait or walking manner is a biometric feature that allows identification of a person when other biometric features such as the face or iris are not visible. In this paper, we present a new pose-based convolutional neural network model for gait recognition. Unlike many methods that consider the full-height silhoue...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	82,784
2401.06925	Modeling Latent Selection with Structural Causal Models	Selection bias is ubiquitous in real-world data, and can lead to misleading results if not dealt with properly. We introduce a conditioning operation on Structural Causal Models (SCMs) to model latent selection from a causal perspective. We show that the conditioning operation transforms an SCM with the presence of an ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	421,347
1712.01097	Generalized Grounding Graphs: A Probabilistic Framework for Understanding Grounded Commands	Many task domains require robots to interpret and act upon natural language commands which are given by people and which refer to the robot's physical surroundings. Such interpretation is known variously as the symbol grounding problem, grounded semantics and grounded language acquisition. This problem is challenging b...	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	86,046
2007.01510	MIRA: Leveraging Multi-Intention Co-click Information in Web-scale Document Retrieval using Deep Neural Networks	We study the problem of deep recall model in industrial web search, which is, given a user query, retrieve hundreds of most relevance documents from billions of candidates. The common framework is to train two encoding models based on neural embedding which learn the distributed representations of queries and documents...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	185,457
1804.09288	A Closer Look at Weak Label Learning for Audio Events	Audio content analysis in terms of sound events is an important research problem for a variety of applications. Recently, the development of weak labeling approaches for audio or sound event detection (AED) and availability of large scale weakly labeled dataset have finally opened up the possibility of large scale AED....	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	95,939
2405.06363	Projection by Convolution: Optimal Sample Complexity for Reinforcement Learning in Continuous-Space MDPs	We consider the problem of learning an $\varepsilon$-optimal policy in a general class of continuous-space Markov decision processes (MDPs) having smooth Bellman operators. Given access to a generative model, we achieve rate-optimal sample complexity by performing a simple, \emph{perturbed} version of least-squares val...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	453,272
1202.3215	Data quality measurement on categorical data using genetic algorithm	Data quality on categorical attribute is a difficult problem that has not received as much attention as numerical counterpart. Our basic idea is to employ association rule for the purpose of data quality measurement. Strong rule generation is an important area of data mining. Association rule mining problems can be con...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	14,336
2408.08005	Inversion-DeepONet: A Novel DeepONet-Based Network with Encoder-Decoder for Full Waveform Inversion	Full waveform inversion (FWI) plays a crucial role in the field of geophysics. There has been lots of research about applying deep learning (DL) methods to FWI. The success of DL-FWI relies significantly on the quantity and diversity of the datasets. Nevertheless, existing FWI datasets, like OpenFWI, where sources have...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	480,815
2106.08590	Domain Consistency Regularization for Unsupervised Multi-source Domain Adaptive Classification	Deep learning-based multi-source unsupervised domain adaptation (MUDA) has been actively studied in recent years. Compared with single-source unsupervised domain adaptation (SUDA), domain shift in MUDA exists not only between the source and target domains but also among multiple source domains. Most existing MUDA algor...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	241,345
2204.14079	Fix the Noise: Disentangling Source Feature for Transfer Learning of StyleGAN	Transfer learning of StyleGAN has recently shown great potential to solve diverse tasks, especially in domain translation. Previous methods utilized a source model by swapping or freezing weights during transfer learning, however, they have limitations on visual quality and controlling source features. In other words, ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	294,047

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